A hybrid of real coded genetic algorithm and artificial fish swarm algorithm for short-term optimal hydrothermal scheduling

• Published in: International journal of electrical power & energy systems Vol. 62; pp. 617 - 629
• Main Authors: Fang, Na, Zhou, Jianzhong, Zhang, Rui, Liu, Yi, Zhang, Yongchuan
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.11.2014; Elsevier
• Subjects: Algorithms; Applied sciences; Artificial fish swarm algorithm; Constraints handling; Electrical engineering. Electrical power engineering; Exact sciences and technology; Fish; Genetic algorithms; Mathematical models; Optimization; Real coded genetic algorithm; Scheduling; Searching; Self-propagating synthesis; Short-term hydrothermal scheduling; Constraints handling; Artificial fish swarm algorithm; Real coded genetic algorithm; Short-term hydrothermal scheduling; Short term; Performance evaluation; Costs; Optimization method; Scheduling; Nonlinear problems; Water flow; Penalty function; Computation time; Genetic algorithm; Optimal solution; Inequality constraint; Equality constraint; System simulation; Feasibility; Delay time; Optimal planning; Effectiveness factor; Comparative study
• ISSN: 0142-0615; 1879-3517
• DOI: 10.1016/j.ijepes.2014.05.017

•We present a SHS model to analyze the operation of hydrothermal power system.•Transmission losses, ramp rate limits and prohibited discharge zones are considered.•We propose a hybrid algorithm combining RCGA with AFSA.•We present the coarse and fine adjustment methods to deal with the constraints.•The results show that the proposed method can provide better solution. The short-term hydrothermal scheduling (SHS) is a complicated nonlinear optimization problem with a series of hydraulic and electric system constraints. This paper presents a hybrid algorithm for solving SHS problem by combining real coded genetic algorithm and artificial fish swarm algorithm (RCGA–AFSA), which takes advantage of their complementary ability of global and local search for optimal solution. Real coded genetic algorithm (RCGA) is applied as global search, which can explore more promising solution spaces and give a good direction to the global optimal region. Artificial fish swarm algorithm (AFSA) is used as local search to obtain the final optimal solution for improving the exploitation capability of algorithm. The water transport delay between connected reservoirs is taken into account in this paper. Moreover, new coarse and fine adjustment methods without any penalty factors and extra parameters are proposed to deal with all equality and inequality constraints. To verify the feasibility and effectiveness of RCGA–AFSA, the proposed method is tested on two hydrothermal systems. Compared with other methods reported in the literature, the simulation results obtained by hybrid RCGA–AFSA are superior in fuel cost and computation time.